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Aim: The interdependencies between trophic interactions, environmental factors and anthropogenic forcing determine how species distributions change over time. Large changes in species distributions have occurred as a result of climate change. The objective of this study was to analyse how the spatial distribution of cod and flounder have changed in the Baltic Sea during the past four decades characterized by large hydrological changes. Location: Baltic Sea Taxon: Cod (Gadus morhua) and flounder (Platichthys flesus) Methods: Catch per unit of effort (CPUE) data for adult and juvenile cod and for adult flounder were modelled using Delta-Generalized additive models including environmental and geographical variables between 1979 and 2016. From the annual CPUE predictions for each species, yearly distribution maps and depth distribution curves were obtained. Mean depth and the depth range were estimated to provide an indication on preferred depth and habitat occupancy. Results: Adult and juvenile cod showed a contraction in their distribution in the southern areas of the Baltic Sea. Flounder, instead, showed an expansion in its distribution with an increase in abundance in the northern areas. The depth distributions showed a progressive shift of the mean depth of occurrence towards shallower waters for adult cod and flounder and towards deeper waters for juvenile cod, as well as a contraction of the species depth ranges, evident mainly from the late 1980s. Main conclusions: Our study illustrates large changes in the spatial distribution of cod and flounder in the Baltic Sea. The changes in depth distribution occurred from the late 1980s are probably due to a combination of expanded areas of hypoxia in deep waters and an increase in predation risk in shallow waters. The net effect of these changes is an increased spatial overlap between life stages and species, which may amplify cod cannibalism and the interaction strength between cod and flounder. Data_cod_flounderAdult cod, juvenile cod and flounder CPUE data (in grams). The datasets contain also the associated bottom temperature, salinity and oxygen concentration of each haul as well as the latitude and longitude in decimal degrees.

European Seabirds At Sea (ESAS) assembles offshore monitoring data on seabirds and marine mammals. This international database mostly includes data from the North Sea, yet large parts of the northeastern Atlantic Ocean are covered as well. It finds its origin in the 'Seabirds at Sea' project, which was initiated in 1979 following the discovery of major oil potential in the North Sea and an urgent need to gain more knowledge on the occurrence and distribution of seabirds in their offshore habitat. This led to the execution of large-scale ship-based surveys across the North Sea using a standardized data collection method and a first European-wide data assembly in 1991. ESAS data are collected by various partners during aerial or ship-based surveys at sea and according to a methodology that allows to calculate georeferenced seabird densities. Standard practice further implies collecting as much information as possible on animal age, plumage and behaviour as well as observation conditions and distance to the observed individuals. As part of the WOZEP research project, ESAS data were migrated in 2022 from its former host JNCC (UK) to ICES. The ICES infrastructure allows partners to submit new data and users to download or request data.

Additional file 1: Figure S1. Whole-head serial sectioning and staining for FoxG1. All coronal paraffin sections ordered from ventral to dorsal. The dark anterior pigment corresponds to the frontal eye (FE). Apart from the expression in the brain, FoxG1 is also expressed in some cells of the floor plate (FP) and some ventrolateral cells of the central canal. We also observed expression in somites (M), as described previously by Toresson et al., 1998. Abbreviations: CC: Central Canal; FP: Floor plate; FE: Frontal eye; IO: Infundibular organ; M: Muscle; N: Notochord; NP: Neuropore.

CzIPMR code to estimate the recovery time for Cystoseira zosteroides populations after a major disturbance at different temperature scenarios treatments. In addition, stochastic population growth rate (λs) and quasi-extinction probability at increasing frequency of two major disturbances at increasing temperature scenarios. These analyses correspond to the figures 4 and 5 of Capdevila et al. 2018 JEcol.MixedEffectsparamsParameter values needed for the Integral Projection Models used to model the life cycle and population dynamics of Cystoseira zosteroides. This includes seven demographic processes: 1.survival (σ), 2.growth (γ), 3.fertility (φ), 4.recruits per capita (δ(N)), 5.probability of settlement of recruits (ε), 6.early survival of recruits (σs) and 7.recruits size probability distribution.IPMFunctionsFunctions required to run the CzIPM.R script. This script contains the description of the growth, survival and fecundity functions used to build the IPMs.1. The best-fitted model for survival (σ) was a logistic mixed effect model including size as fixed factors and population nested in years as a random factor. 2. For growth (γ), the best-fitted model was a linear mixed effect model, with size as fixed factor and population nested in year as random factor. 3. Fertility (φ(z)), was estimated as the relation between reproductive status (reproductive vs. non-reproductive) and size with a binomial regression. 4. Recruitment per capita (δ(N)) is density-dependent in C. zosteroides (Capdevila et al., 2015), so a generalized linear model with Poisson error distribution and a log-link function was fitted, correlating the recruit:adult ratio as a function of the adult density. 5. To model the effect of temperature on the probability of settlement (ε) we used a generalized linear mixed models (GLMM), with a Poisson error distribution and a logit link function, the independent variable was the number of zygotes, temperature was treated as a fixed variable and we used the ID of each quadrat of the Petri dishes as a random variable. 6. To model the effect of temperature and time (fixed factors) on germling survival (σs), we used a GLMM with a binomial error distribution and a logit link function, with the ID of each quadrat of each Petri dish as a random variable to deal with the lack of independence between observations repeated at different times and a binomial error distribution was assumed to deal with the binary response variable (survive vs. die). 7. The size distribution of recruits was estimated as a normal probability function. In addition, the function required to project the density-dependent and stochastic IPMs is provided.modsumDensity-dependent function, relating the number of Cystoseira zosteroides recruits with the number of adults. It is a generalized linear model (GLM) with Poisson error distribution and a log-link function, correlating the recruit:adult ratio with the adult density. This file is needed to run the code CzIPM.R.settData on the impacts of temperature (16ºC, 20ºC and 24ºC) on the settlement of Cystoseira zosteroides early stages. This file is needed to perform the projections in CzIPM.R code.survrecData on the impacts of the temperature treatments (16ºC, 20ºC and 24ºC) to early survival of Cystoseira zosteroides. This file is required to run the code CzIPM.R. 1. Understanding the combined effects of global and local stressors is crucial for conservation and management, yet challenging due to the different scales at which these stressors operate. Here we examine the effects of one of the most pervasive threats to marine biodiversity, ocean warming, on the early life stages of the habitat-forming macroalga Cystoseira zosteroides, its long-term consequences for population resilience and its combined effect with physical stressors. 2. First, we performed a controlled laboratory experiment exploring the impacts of warming on early life stages. Settlement and survival of germlings were measured at 16ºC (control), 20ºC and 24ºC and both processes were affected by increased temperatures. Then, we integrated this information into stochastic, density-dependent integral projection models (IPM). 3. Recovery time after a minor disturbance significantly increased in warmer scenarios. The stochastic population growth rate (λs) was not strongly affected by warming alone, as high adult survival compensated for thermal-induced recruitment failure. Nevertheless, warming coupled with recurrent physical disturbances had a strong impact on λs and population viability. 4. Synthesis: The impact of warming effects on early stages may significantly decrease the natural ability of habitat-forming algae to rebound after major disturbances. These findings highlight that, in a global warming context, populations of deep-water macroalgae will become more vulnerable to further disturbances, and stress the need to incorporate abiotic interactions into demographic models.

Pressione media al suolo (Pa). Corsa del 2024-01-01 ore 12 UTC - Valido dalle ore 12 UTC del 2024-01-01 alle ore 00 UTC del 2024-01-05. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora. Средно налягане на земята (Pa). 2024—01—01 12 UTC — валиден от 2024—01—01 12 UTC до 2024—01—05 00 UTC. Метеорологичен модел WRF (модел за изследване и прогнозиране на Weather), ARW ядро (версия 3.2) с пространствена разделителна способност при 3 km, времева разделителна способност 60 часа, интервал 1 час. Vidējais zemes spiediens (Pa). 2024-01-01 12 UTC - spēkā no 2024-01-01 12 UTC līdz 2024-01-05 00 UTC. WRF meteoroloģiskais modelis (laika apstākļu izpētes un prognozēšanas modelis), ARW kodols (3.2. versija) ar telpisko izšķirtspēju 3 km attālumā, pagaidu izšķirtspēju 60 stundas, intervālu 1 stunda. Pressjoni medja tal-art (Pa). 2024-01-01 12 UTC - Validu mill-2024-01-01 12 UTC sal-2024-01-05 00 UTC. Mudell meteoroloġiku tad-WRF (Mudell ta’ Riċerka u Tbassir tat-Temp), qalba tal-ARW (verżjoni 3.2) b’riżoluzzjoni spazjali ta’ 3 km, riżoluzzjoni temporali ta’ 60 siegħa, intervall ta’ siegħa. Vidutinis žemės slėgis (Pa). 2024-01-01 12 UTC - Galioja nuo 2024-01-01 12 UTC iki 2024-01-05 00 UTC. WRF meteorologinis modelis (orų tyrimų ir prognozavimo modelis), ARW šerdis (3.2 versija), kurios erdvinė skiriamoji geba yra 3 km, laiko skiriamoji geba – 60 valandų, intervalas – 1 val. Presiunea medie la sol (Pa). 2024-01-01 12 UTC - Valabil de la 2024-01-01 12 UTC până la 2024-01-05 00 UTC. Modelul meteorologic WRF (modelul de cercetare și prognoză meteorologică), nucleul ARW (versiunea 3.2) cu rezoluție spațială la 3 km, rezoluție temporală 60 de ore, interval 1 oră. Durchschnittlicher Bodendruck (Pa). 2024-01-01 12 UTC - Gültig von 2024-01-01 12 UTC bis 2024-01-05 00 UTC. WRF-Meteorologisches Modell (Wetterforschungs- und Prognosemodell), ARW-Kern (Version 3.2) mit räumlicher Auflösung bei 3 km, zeitlicher Auflösung 60 Stunden, Intervall 1 Stunde. Średnie ciśnienie gruntu (Pa). 2024-01-01 12 UTC - Ważny od 2024-01-01 12 UTC do 2024-01-05 00 UTC. Model meteorologiczny WRF (Weather Research and Forecasting model), rdzeń ARW (wersja 3.2) z rozdzielczością przestrzenną na 3 km, rozdzielczość czasową 60 godzin, interwał 1 godzina. Povprečni tlak na tleh (Pa). 2024-01-01 12 UTC – velja od 2024-01-01 12 UTC do 2024-01-05 00 UTC. Meteorološki model WRF (model vremenskih raziskav in napovedi), jedro aluminijastih koles (različica 3.2) s prostorsko ločljivostjo 3 km, časovno ločljivostjo 60 ur, intervalom 1 ure. Presión media sobre el suelo (Pa). 2024-01-01 12 UTC - Válido desde 2024-01-01 12 UTC hasta 2024-01-05 00 UTC. Modelo meteorológico WRF (Weather Research and Forecasting model), núcleo ARW (versión 3.2) con resolución espacial a 3km, resolución temporal 60 horas, intervalo 1 hora.

Raw data acquired by a JAVAD Delta 3 GNSS Receiver with a resolution of 50Hz on board RV Polarstern during the MOSAiC expedition.The data is stored in JAVAD's .jps format (JAVAD GNSS, GREIS – GNSS Receiver External Interface Specification, San Jose, USA, 2022 (download available at https://www.javad.com/jgnss/support/manuals.html)). Each file contains data of one day with a typical file size of 1GB. The receiver was equipped with a multi-frequency GNSS antenna which was mounted on the ship's observation deck. It has to be noted that the main mast and the ship's chimney lead to a shadowing of GNSS observation links in at least two directions seen from the antenna position. The data was collected for measuring scintillation activitiy in the central arctic. Processed scintillation measurements with a 1 minute resolution are available (doi:10.1594/PANGAEA.956019).

WET Labs investigated the bias found in Poteau et al. 2017: http://dx.doi.org/10.1002/2017gl073949 and provides a matrix of affected sensors with scale factors for the backscattering channels using a correct weighted phase function constant values for ECO sensors mounted on BGC-Argo floats.

From 1983 until 1989 NOAA-NCEI compiled the NOAA-MMS Marine Minerals Geochemical Database from journal articles, technical reports and unpublished sources from other institutions. At the time it was the most extended data compilation on ferromanganese deposits world wide. Initially published in a proprietary format incompatible with present day standards it was jointly decided by AWI and NOAA to transcribe this legacy data into PANGAEA. This transfer is augmented by a careful checking of the original sources when available and the encoding of ancillary information (sample description, method of analysis...) not present in the NOAA-MMS database.This dataset represents the digitized Table 1, pp. 68, of the related publication. During a short geological cruise in May 1979 by HMAS Kimbla, two manganese nodules were recovered from the deep sea about 250 nautical miles southeast of Sydney. They were laying on a greenish gray calcareous mud. The manganese nodules are subspherical with a rather irregular and rough surface, and are about 10 em in diameter. They consist of numerous concentric shells, many less than 0.1 mm thick, of brown and black metal oxides, and pale yellowish clay, in roughly equal proportions. The metal contents of the nodules (dried at 105°C) were determined at the Australia Bureau of Mineral Resources, Geology and Geophysics (BMR) using the atomic absorption method for Mn, Ni, Cu and Co, and a volumetric method for Fe.